Springer Science and Business Media LLC

This paper describes an approach to using evolutionary algorithms for reasoning about paths through network data. The paths investigated in the context of this research are functional paths wherein the characteristics (e.g., path length, morphology, location) of the path are integral to the objective purpose of the path. Using two datasets of combined surface and road networks, the research demonstrates how an evolutionary algorithm can be used to reason about functional paths. We present the algorithm approach, the parameters and fitness function that drive the functional aspects of the path, and an approach for using the algorithm to respond to dynamic changes in the search space. The results of the search process are presented in terms of the overall success based on the response of the search to variations in the environment and through the use of an occupancy grid characterizing the overall search process. The approach offers a great deal of flexibility over more conventional heuristic path finding approaches and offers additional perspective on dynamic network analysis.

The wide and increasing availability of collected data in the form of trajectories has led to research advances in behavioral aspects of the monitored subjects (e.g., wild animals, people, and vehicles). Using trajectory data harvested by devices, such as GPS, RFID and mobile devices, complex pattern queries can be posed to select trajectories based on specific events of interest. In this paper, we present a study on FPGA- and GPU-based architectures processing complex patterns on streams of spatio-temporal data. Complex patterns are described as regular expressions over a spatial alphabet that can be implicitly or explicitly anchored to the time domain. More importantly, variables can be used to substantially enhance the flexibility and expressive power of pattern queries. Here we explore the challenges in handling several constructs of the assumed pattern query language, with a study on the trade-offs between expressiveness, scalability and matching accuracy. We show an extensive performance evaluation where FPGA and GPU setups outperform the current state-of-the-art (single-threaded) CPU-based approaches, by over three orders of magnitude for FPGAs (for expressive queries) and up to two orders of magnitude for certain datasets on GPUs (and in some cases slowdown). Unlike software-based approaches, the performance of the proposed FPGA and GPU solutions is only minimally affected by the increased pattern complexity.

Given a collection of regions on a map, we seek a method of continuously altering the regions as the scale is varied. This is formalized and brought to rigor as well-defined problems in homotopic deformation. We ask the regions to preserve topology, area-ratios, and relative position as they change over time. A solution is presented using differential methods and computational geometric techniques. Most notably, an application of this method is used to provide an algorithm to obtain cartograms.

A major focus of research in recent years has been the development of algorithms for automated line smoothing. However, combination of the algorithms with other generalization operators is a challenging problem. In this research a key aim was to extend a snakes optimization approach, allowing displacement of lines, to also be used for line smoothing. Furthermore, automated selection of control parameters is important for fully automated solutions. An existing approach based on line segmentation was used to control the selection of smoothing parameters dependent on object characteristics. Additionally a new typification routine is presented, which uses the same preprocessed analysis for the segmentation of lines to find suitable candidates from curve bends. The typification is realized by deleting undersized bends and emphasizing the remaining curve bends. The main results of this research are two new algorithms for line generalization, where the importance of the line smoothing algorithm lies in the usage of a optimization approach which can also be used for line displacement.

Given a set S of sites and a set O of objects in a metric space, the Optimal Location (OL) problem is about computing a location in the space where introducing a new site (e.g., a retail store) maximizes the number of the objects (e.g., customers) that would choose the new site as their “preferred” site among all sites. However, the existing solutions for the optimal location problem assume that there is only one criterion to determine the preferred site for each object, whereas with numerous real-world applications multiple criteria are used as preference measures. For example, while a single criterion solution might consider the metric distance between the customers and the retail store as the preference measure, a multi-criteria solution might consider the annual membership cost as well as the distance to the retail store to find an optimal location. In this paper, for the first time we develop an efficient and exact solution for the so-called Multi-Criteria Optimal Location (MCOL) problem that can scale with large datasets. Toward that end, first we formalize the MCOL problem as maximal reverse skyline query (MaxRSKY). Given a set of sites and a set of objects in a d-dimensional space, MaxRSKY query returns a location in the space where if a new site s is introduced, the size of the (bichromatic) reverse skyline set of s is maximal. To the best of our knowledge, this paper is the first to define and study MaxRSKY query. Accordingly, we propose a filter-based solution, termed EF-MaxRSKY, that effectively prunes the search space for efficient identification of the optimal location. Our extensive empirical analysis with both real and synthetic datasets show that EF-MaxRSKY is invariably efficient in computing answers for MaxRSKY queries with large datasets containing thousands of sites and objects.

Spatial computing is a set of ideas, solutions, tools, technologies, and systems that transform our lives with a new prospect of understanding, navigating, visualizing and using locations. In this community whitepaper, we present a perspective on the changing world of spatial computing, research challenges and opportunities and geoprivacy issues for spatial computing. First, this paper provides an overview of the changing world of spatial computing. Next, promising technologies that resulted from the integration of spatial computing in the everyday lives of people is discussed. This integration results with promising technologies, research challenges and opportunities and geoprivacy issues that must be addressed to achieve the potential of spatial computing.